The Duration in Treatment With Electroconvulsive Therapy Among Patients Screening Positive or Negative for Borderline Personality Disorder Traits
Bibliographic record
Abstract
OBJECTIVE: While emerging evidence suggests that electroconvulsive therapy (ECT) is an effective treatment for depressive symptoms in patients with co-occurring borderline personality disorder (BPD) traits, it is unclear whether the presence of BPD traits modulates the tolerability of ECT. This study estimates the association between BPD traits and retention in acute course ECT treatment. METHODS: This study used a retrospective cohort of patients receiving ECT between 2015 and 2020 and who were assessed using the McLean Screening Instrument for BPD, the Quick Inventory of Depressive Symptomatology Self-Report 16-item scale, and the Montreal Cognitive Assessment before initiating treatment. RESULTS: One thousand five hundred eight patients received ECT during the study period, of whom 277 (18.4%) screened positive for BPD traits. Borderline personality disorder traits were associated with a higher odds of remaining in ECT for at least 10 treatments (adjusted odds ratio, 1.502; 95% confidence interval, 1.11-2.02; P = 0.007). Among individual symptom domains, only endorsing chronically feeling empty was associated with duration in ECT treatment. CONCLUSIONS: Among patients receiving ECT, screening positive for BPD traits was associated with a higher odds of receiving at least 10 ECT treatments. These results support the overall tolerability of ECT in patients with BPD traits.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".